Network capacity forecasting and maintenance

ABSTRACT

Media, systems, and methods for ensuring adequate data-processing capacity in a communications network are provided. An embodiment of the method includes identifying a communications resource to evaluate, determine a maximum capacity that the resource is capable of handling, projecting a capacity-exhaustion timeline, wherein the timeline includes time estimations that are adjusted by an adjusting factor that is based at least in part on an accuracy of prior projection estimates. Determining a more accurate capacity exhaustion timeline allows for more accurate comparison against a time required to add such capacity. Recommends to effect capacity additions can be provided.

INTRODUCTION

Telecommunications networks include an array of resources. Variousresources such as trunks and switches that connect to trunks havecertain capacities of data that can be handled. If these capacities areexceeded beyond a threshold amount then problems can occur in thetelecommunications network. For example, calls may be dropped, lost, orthe overall integrity of the network may be compromised. Because anetwork cannot be upgraded instantaneously, a need exists to accuratelyforecast capacity demands of network resources so that the network canbe upgraded with enough time so as to reduce the occurrence of errorsassociated with an over-burdened network.

SUMMARY

The presenting invention is defined by the claims below. The presentinvention has several practical applications in the technical artsincluding accurately determining future network-capacity needs so thatdata communications will not be interrupted.

In a first illustrative aspect, a method of ensuring adequatedata-processing capacity in a communications network. The methodincludes identifying a communications trunk to evaluate, determiningwhether the trunk has roll-over capacity; and if the trunk does haveroll-over capacity, then carrying out a first process to forecast a needto add processing capacity; but if the trunk does not have roll-overcapacity, then carrying out a second process to forecast a need to addprocessing capacity. Both of the first and second processes can includeprojecting a date when current processing capacity will be exhaustedbased on an accuracy of prior projections.

In another aspect, a computer-implemented method includes identifying acommunications resource to evaluate, determining a maximum capacity thatthe resource is capable of handling, and projecting acapacity-exhaustion timeline, wherein the timeline includes timeestimations that are adjusted by an adjusting factor that is based atleast in part on an accuracy of prior projection estimates.

In a final illustrative aspect, a computer-implemented method includesidentifying a communications trunk to evaluate, determining a maximumcapacity that the trunk is capable of handling, forecasting when currentcapacity will reach a maximum desired level by considering at least inpart an accuracy of prior forecasting estimates, and presenting anaction time frame that includes a consideration of a procurement timehorizon associated with providing additional capacity.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Illustrative embodiments of the present invention are described indetail below with reference to the attached drawing figures, which areincorporated by reference herein and wherein:

FIG. 1 depicts an illustrative operating environment suitable forpracticing an embodiment of the present invention;

FIG. 2A is an illustrative method for practicing an embodiment of thepresent invention;

FIG. 2B depicts an illustrative process of determining acapacity-exhaustion timeline according to an embodiment of the presentinvention;

FIG. 2C depicts an illustrative method for modifying an initialprojection based on an accuracy of past predictions; and

FIG. 3 depicts an illustrative graphical format for presentingexhaustion-horizon information, and also pictorially illustrates aspectsof the invention to help facilitate understanding of an embodiment ofthe present invention.

DETAILED DESCRIPTION

Throughout the description of the present invention, several acronymsand shorthand notations are used to aid the understanding of certainconcepts pertaining to the associated system and services. Theseacronyms and shorthand notations are solely intended for the purpose ofproviding an easy methodology of communicating the ideas expressedherein and are in no way meant to limit the scope of the presentinvention. The following is a list of these acronyms:

EO End Office GOS Grade of Service IDEN Integrated Data Enhanced NetworkNGVN Next Generation Voice Networks PBX Private Branch Exchange

Embodiments of the present invention may be embodied as, among otherthings: a method, system, or computer-program product. Accordingly, theembodiments may take the form of a hardware embodiment, a softwareembodiment, or an embodiment combining software and hardware. In oneembodiment, the present invention takes the form of a computer-programproduct that includes computer-useable instructions embodied on one ormore computer-readable media.

Computer-readable media include both volatile and nonvolatile media,removable and nonremovable media, and contemplates media readable by adatabase, a switch, and various other network devices. Network switches,routers, and related components are conventional in nature. By way ofexample, and not limitation, computer-readable media comprisecomputer-storage media.

Computer-storage media, or machine-readable media, include mediaimplemented in any method or technology for storing information.Examples of stored information include computer-useable instructions,data structures, program modules, and other data representations.Computer-storage media include, but are not limited to RAM, ROM, EEPROM,flash memory or other memory technology, CD-ROM, digital versatile discs(DVD), holographic media or other optical disc storage, magneticcassettes, magnetic tape, magnetic disk storage, and other magneticstorage devices. These memory components can store data momentarily,temporarily, or permanently.

Combinations of the above are included within the scope ofcomputer-readable media.

FIG. 1 depicts an illustrative operating environment suitable forpracticing an embodiment of the present invention and is referencedgenerally by the numeral 110. By no means are all data sources that canbe coupled to processor 112 shown. Call data 113 stores statistics andother information associated with routing data through a network.Illustrative metrics may include number of calls (or other datarequests) offered to a resource, the number of requests satisfied, thetype of data traffic handled, etc. The amount is vast.

Network-resource data 114 includes data that describes various types ofnetwork resources, characteristics of those resources, and how thoseresources are coupled to other resources. Prior forecast predictions 116include predictions that were made in the past. This data is preservedso that it can be compared against actual results (capacities at certaintime intervals), which can also be stored in database 116.Augmentation-implementation data 118 includes data associated withproviding additional capacity to a network. For example,augmentation-implementation data 118 may include time horizonsassociated with installing new or replacing used equipment, regionalpricing data, availability data, special-circumstances data thatdescribe special measures that need to be taken, and any other detailsthat might be useful to know in making a decision to augment a givenresource.

FIG. 2A depicts an illustrative method suitable for practicing anembodiment of the present invention and is referred to generally by thenumeral 200. Of course not all steps shown in FIG. 2A are necessary.Some steps are shown to provide a better understanding of how anembodiment of the present invention would work. At a step 212, anindication of a trunk to be analyzed is received. A trunk is acommunications line between two or more network-resource components.Illustrative network components include switches, switching systems,routing components, etc.

A switching system may include equipment in a central office (such as atelephone company). Generally, a trunk connects two switching systems.An end office (EO) is a type of central office to which a telephonesubscriber is connected. The term “end office” is often used to refer tothe last central office before a subscriber's phone equipment. It canalso be referred to as the central office that actually delivers dialtone to a subscriber (including VOP—voice over packet—type systems,which are utilized in packet-based networks, and also including trafficassociated with facilitating wireless communications).

A trunk or set of trunks facilitate the communication of data from asource device to a destination device. Trunks are generally rated for acertain capacity. That is, a trunk may be able to satisfy a certaincapacity of data, after which little or no data will be able to passthrough the trunk. One measurement used to measure capacity associatedwith trunks or their corresponding equipment is an “erlang.” Capacitycan be associated with a trunk or with the device to which it connects,such as a switch or switching equipment.

In a telecommunications network, switches and other routing componentsinclude tables that describe how information is to be routed throughoutthe network. Illustrative communication switches may be made by avariety of companies and are associated with a variety of technologies.For example, a first switch may be an IDEN switch, or an IDEN gatewayswitch, or an NGVN switch, and/or a switch manufactured by variousmanufacturers. These switches are generally coupled directly orindirectly to databases that record statistics and other informationassociated with the switches. For example, these databases may storeusage information, routing information, and other traffic-related data.Other illustrative data may include switch assignments, wireless data,reference data, and the like.

After a trunk is identified at a step 212, a determination is made at astep 214 as to whether the trunk is a final trunk. A trunk is a finaltrunk if it is the last available trunk to handle data communicationsthrough the data communications line(s). Thus, if a trunk does not haveany roll-over capability wherein a secondary device would otherwise beassociated with the trunk to receive and handle data in the event thatthe trunk cannot handle such data, then that trunk would be a finaltrunk. In one embodiment, if the trunk is not a final trunk, thenprocessing advances to a step 216, where a determination is made as towhether the trunk of step 212 is a direct end office trunk.

In the embodiment where check 216 is performed, if the trunk is not adirect EO trunk, then processing stops as no further determination needsto be made. But if the trunk is a direct end office trunk, or other typeof trunk that would be appropriate for processing to continue, thenprocessing advances to a step 218, wherein a busiest time interval isdetermined. Step 218 may also be referred to as determining utilizationat the busiest time interval; that is, determining a time intervalassociated with a period of peak activity. For example, in oneembodiment, the fruit of step 218 may be a determination of the busiesthour of the week (or some other desired interval, such as day, or evenan hour or less).

In one embodiment, a busiest time interval may be determined byconsidering an amount of data processed against an available bandwidthwhile processing the data. As previously mentioned, call data can bestored in one or more data stores associated with a communicationsnetwork, such as database 113. Such a database 113 can be queried todetermine a certain number of calls that a piece of equipment wassupposed to service, a number of calls that were blocked, and othertraffic-related data over various time intervals. With this dataavailable, the maximum utilization can be determined. This maximumutilization may be greater than 100%. This may be the case because thenumber of rollover calls can be taken into consideration.

In such a situation, if a switch was able to service a certain number ofcalls but also had to turn away a number of calls, then if those numberof calls that are turned away are included in a calculation to determinea utilization value, then that value may be greater than 100%. Forexample, if a switch needed to turn away 40 out of 140 calls offered,then for a given time interval that those calls were received, themaximum utilization could be 140%. In some embodiments, it may be thecase that a trunk or other network resource is not to be replaced untilsuch a value exceeds a threshold percentage, such as 150% or 200%. Thisis the case because a direct EO trunk should rollover to another trunkuntil the rollover costs are greater than the cost for procuringadditional facilities.

But at a step 220, a determination is made as to whether augmentationshould be considered. This occurs by comparing the utilization ceilingwith an actual maximum utilization. The utilization ceiling is ameasurement or threshold set by a telecommunications company thatindicates a highest tolerable capacity. This maximum tolerableutilization will be compared against an actual peak utilization todetermine whether augmentation is appropriate. For example, if themaximum tolerable utilization is 175%, but an actual peak utilization isdetermined to only be 160%, then augmentation based on those metricswould not be desired. Of course, if the maximum tolerable utilizationhad been set lower than the peak utilization, then processing can atleast continue to determine whether the trunk should be augmented. Insuch a situation, processing would advance to a determination step 222,wherein a determination is made as to whether augmenting the trunk wouldbe cost-efficient. Thus, an embodiment of the present invention includesan automatic method for determining whether augmenting a networkresource would be cost-efficient. This determination is made bycomparing the cost of the procuring new facilities to the cost of therollover calls.

For example, the present invention will be able to determine what typeof resources will be necessary to augment the trunk, trunk group, orequipment associated with the communications pathway. In an embodimentof the present invention that includes a database of historical datathat relates to prior implementation times and costs, a determinationcan be made as to the projected amount of time and cost associated withimplementing at least a first level of augmentation. For example, afirst level of augmentation may include doubling the resources that arecurrently available. But the present invention will also be able toconsider the costs currently being incurred as a result of theapplicable trunk not being able to service all of the data offered toit.

As previously mentioned, a trunk may not be able to service all of thedata offered to it. In such a situation, data is rolled over to anothercommunications line. In some situations, a telecommunications companymay not own this other communications line, and there may be feesassociated with having that data serviced by some other roll-overcommunications line. For example, a first company may own a first trunkgroup, and a second company may own a roll-over trunk group. The firstcompany may need to pay fees to the second company every time data isrolled over from the first trunk group to the roll-over trunk group.Armed with this knowledge, a meaningful decision can be made as towhether it is cost-efficient to augment current resources or to justallow the status quo. In a situation where augmentation would cost less(perhaps over a certain time horizon) than the status quo, then anembodiment of the present invention will automatically recommendaugmenting the current network without user intervention. In oneembodiment, low-level details associated with augmenting currentresources can be analyzed.

For example, prices may vary with geographical regions. A first switchmay cost a first price to be implemented in a first geographical regionbut may cost a second price to be implemented in a second geographicalregion because of various factors. Illustrative factors include the costof labor, the cost of availability of resources, and/or the costsassociated with reaching a remote or other hard-to-reach location. Anembodiment of the present invention can automatically, without userintervention, assimilate all such variables in making an informedrecommendation as to whether augmentation should occur or not. This stepis referenced by numeral 224, wherein an appropriate entity is notified.An appropriate entity may be a specific network engineer or anotherpiece of equipment such that an alarm or automatic notification isdirected appropriately so that the augmentation recommendation can beacted upon. At a step 226, provided more trunks exist to be examined,processing reverts to step 214 after a new trunk is identified to beexamined.

Much of the processing steps described above were carried out inresponse to a determination that the trunk at issue was not a finaltrunk. But if a determination is made at step 214 that the trunk atissue is a final trunk, then processing advances to a step 228 todetermine a maximum capacity for a given grade of service. A grade ofservice (GOS) is a benchmark indicator that indicates an acceptablenumber of errors associated with processing communications transactions.For example, it may be the case that 5 out of 1,000 transactions is themaximum number of transactions that can be associated with errors in acommunications-processing environment. In such a case, the GOS may bereferred to as 0.005. It may be the case that a trunk may have a greatercapacity if more errors are tolerated.

Accordingly, a specific trunk will have a maximum capacity associatedwith a given GOS. An illustrative unit, among others, for capacity maybe the erlang. For the purposes of this description, an erlang will beused as an illustrative unit of measure. An erlang is a unit ofmeasurement of traffic density in a telecommunications system.Generally, an erlang describes the total traffic volume of one hour. Forexample, assume that in a given hour 60 calls occur. Each call lastsfive minutes. In such a situation, the minutes of traffic in the hourwill equal the number of calls times their duration, which would be 300in this case (60×5). Accordingly, there would be 300 minutes of trafficin the hour. When this data is normalized to determine the hours oftraffic in the hour, 300 is divided by 60 to reach a result of 5.Accordingly, the traffic figure for the above situation would correspondto 5 erlangs. Thus, at a step 228, a maximum capacity, given in erlangs,is determined based on a grade of service associated with a trunk.

At a step 230, a capacity-exhaustion timeline is determined. Acapacity-exhaustion timeline is a timeline that projects differentlevels of capacity over time. An illustrative process for determining atimeline that maps out projected capacity over time is provided in FIG.2B. Turning now to FIG. 2B, a determination is made at a step 230A as tothe amount of traffic offered, for example, by customers. “Offered”traffic refers to the amount of traffic that was at least initially tobe handled by the trunk or trunk group or resource device. The “carried”amount refers to the amount of traffic that was actually handled by thetrunk. Thus, capacity can be determined for various time intervals byconsidering the amount of offered traffic as well as the amount oftraffic actually carried. Thus, at a step 230B, an embodiment of thepresent invention loops through as many data records or over as manytime intervals as are desired to determine a set of data points, whichare illustratively shown on FIG. 3 and referenced by numeral 312.

We will make continuing reference to both FIG. 2B and FIG. 3 to helpexplain a portion of an embodiment of the present invention. Data points312 are plotted on an axis where time is on the abscissa and capacity onthe ordinate. Thus, each data point corresponds to a certain capacityover a certain time interval. For example, a given time interval may beweekly. In such a situation, each of the data points 312 corresponds toa maximum capacity for that week. Data point 312A may correspond to amaximum capacity for a certain week. Data point 312B may correspond to amaximum capacity of a different week, etc. As many data points can begenerated as are desired.

At a step 230C, regression analysis is used to project an initialcapacity timeline (230D), which is referenced as a dashed line bynumeral 314. Line 314 reflects an initial, unadjusted timelineprojection based on historical peak-capacity values over certainintervals of time. Thus, at a step 230D, future growth, such as the weekin which a certain maximum capacity 316 would be reached can becalculated. In the example of FIG. 3, this would be indicated by line318 which reflects an unadjusted projection of when capacity 316 wouldbe reached.

But the present invention is not limited to merely projecting a capacitytime-frame based on prior peak values. Although those can be aningredient in the present invention, the present invention also includesthe ability to adjust the initial projection 314 based on the accuracyof prior projections. Prior projection points are stored in anembodiment of the present invention. These prior projections are laterevaluated against actual values to determine an adjustment factor, whichcan be applied to the initial projection 314 to arrive at an adjustedprojection horizon 322. In the embodiment shown in FIG. 3, such anadjusted projection results in a steeper projection timeline 322. But itcould be the case that prior predictions were inaccurate in such a waythat the slope of initial projection line 314 should actually be lower,which would result in an adjusted projection line 322 less steep thaninitial projection line 314. By utilizing the adjusted timeline 322, adetermination can be made based on the maximum capacity desired 316 thatan adjusted timeframe 324 is more appropriate than an unadjusted amount320. This data point would be reached by following line 326 down fromthe adjusted timeline 322.

In some embodiments, the adjustment factor is an average of thevariances of prior projections from their actual values. For example, ifthe average prediction over the last 15 weeks was high by 3% on average,then an adjustment factor of 3% can be applied to the initial timeline322 to compensate for an anticipated over-projection. This aspect ofutilizing the accuracy, or, better stated, the inaccuracy, of priorprojections to arrive at a more accurate projection of capacityexhaustion is a useful aspect of an embodiment of the present invention,which is summarily referenced by numeral 230E on FIG. 2B, and moreparticularly shown in FIG. 2C.

Turning now to FIG. 2C, an illustrative process for modifying capacitypredictions based on the accuracy of prior predictions is provided andgenerally referred to by the numeral 230E. At a step 230E.1, historicalperformance data is retrieved. At a step 230E.2, the historicalperformance data is compared against past predictions that correspond tothe historical performance data. As previously mentioned, this data canbe stored in various tables and be accessible for querying. At a step230E.3, an appropriate correction factor is determined. Alternatively,initial projection 314 can be directly modified according to thecorrection determination of step 230E.3. As previously mentioned, onemethod of calculating an appropriate correction factor is to derive anaverage or weighted average of historical variances. In one embodiment,predictions that were made with more data may be given a differentweight than predictions made with less data. In another embodiment,predictions that were made with data having a more consistent patternmay be given a different weight than predictions made with data having aless consistent pattern. Thus, at a step 230E.4, the results are used tomodify the initial projection.

Returning to FIG. 2B, an exhaustion time-frame can be determined basedon the modification at a step 230F. This value is referenced by numeral324 in FIG. 3. At a step 230G, the results of the aforementionedanalysis can be provided in graphical form. The graphical representationof the history and remaining time until capacity is reached can bedisplayed and presented to a user automatically without userintervention.

Returning to FIG. 2A, at a step 232, the forecasted time projection canbe compared against the time necessary to augment the trunk. In oneembodiment, a relevant time measurement may be a vendor interval. Avendor interval may be the interval of time necessary to upgraderesources associated with the vendor. In this aspect of the invention,much waste can be eliminated by comparing the amount of time it willtake to actually augment a network by a desired amount with the amountof time until exhaustion will occur. In situations where the time-framesare close, a determination can be made to augment the network by agreater amount so that by the time the augmentations are actuallyimplemented a desired amount of time will still exist before futureaugmentation will be necessary or desired.

An illustrative example is provided. Say the aforementioned analysisestimates that an 100% (or some other percentage if desired, such as80%) capacity will be reached in 10 weeks given current capacityprojections. In an embodiment of the present invention, historical dataassociated with implementation times for implementing upgrades will bereferenced to determine a time-frame associated with implementing aproposed augmentation. Say that time-frame is 9 weeks. In such a case,if a recommendation was only made on the need to augment, then it couldbe the case that by the time an initial augmentation is implemented,only 1 week would remain until a new analysis would be appropriate. Whatis particularly relevant is what will be the capacity of the augmentednetwork at the time that the augmentation is complete.

The present invention can calculate such a value and determine whetheradditional augmentation is appropriate. For example, the presentinvention could extrapolate the capacity of the given trunk or resourceafter the anticipated augmentation is implemented, and reproject a newtime horizon for still another capacity time-frame. In situations wherethat subsequent time-frame is still sufficiently small, the initialaugmentation recommendation can be supplemented or changed to reflect amore aggressive augmentation.

Based on the more aggressive augmentation, the analysis can be rerun,and still another projection timeline (such as 314 or 322) can bedetermined so that a new time-frame can be known that will correspond tothe need for a subsequent augmentation after augmenting the network afirst time. In this manner, resources can be conserved and not wasted.At a step 234, specific recommendations regarding augmentation will beautomatically presented to a user. Thus, if 250% more capacity should beadded such that adding that amount of capacity would forestall anadditional augmentation in an additional year, such a recommendation canbe presented to a user or other appropriate entity. As mentioned, agreat deal of information about the network, historical data associatedwith implementing various devices, labor costs, construction costs, etc.can be linked to make such a recommendation without user intervention.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the spiritand scope of the present invention. Embodiments of the present inventionhave been described with the intent to be illustrative rather thanrestrictive. Alternative embodiments will become apparent to thoseskilled in the art that do not depart from its scope. A skilled artisanmay develop alternative means of implementing the aforementionedimprovements without departing from the scope of the present invention.

It will be understood that certain features and subcombinations are ofutility and may be employed without reference to other features andsubcombinations and are contemplated within the scope of the claims. Notall steps listed in the various figures need be carried out in thespecific order described.

1. Computer-storage media having computer-useable instructions embodiedthereon for facilitating a method of ensuring adequate data-processingcapacity in a communications network, the method comprising: identifyinga plurality of communications trunks to evaluate; determining whether afirst communications trunk having roll-over capacity is a directend-office trunk, and when it is, carrying out a first process toforecast a need to add processing capacity, wherein the first processincludes: (1) determining a maximum tolerable utilization for the firstcommunications trunk, wherein the maximum tolerable utilization includestraffic that is acceptable to overflow from the first communicationstrunk, and wherein the first process accommodates a determined maximumtolerable utilization in excess of 100%, (2) determining an actual peakutilization for the first communications trunk based on an amount ofutilization during a period of peak activity, (3) comparing the actualpeak utilization with the maximum tolerable utilization, and (4) if theactual peak utilization exceeds the maximum tolerable utilization, thencontinuing to forecast the need to add processing capacity, but (5) ifnot, then stopping the forecast; carrying out a second process for asecond communications trunk having no roll-over capacity to forecast aneed to add processing capacity, projecting a date for each of the firstprocess and the second process when current processing capacity will beexhausted based on an accuracy of prior projections; and providing anotification to an appropriate entity of the projected first processdate and the projected second process date.
 2. The media of claim 1,wherein the first communications trunk and the second communicationstrunk each include one or more communications lines to be evaluated. 3.The media of claim 1, further comprising at least one secondary resourceassociated with the first communications trunk to handle transactionsthat were originally to be handled by the first communications trunk, inthe event that the first communications trunk lacks capacity to handlethe transactions.
 4. The media of claim 3, wherein the at least onesecondary resource includes an additional communications trunk.
 5. Themedia of claim 1, wherein the actual peak utilization includes an amountof traffic that was actually not handled by the first communicationstrunk, and thus allowed to overflow during a peak time interval.
 6. Themedia of claim 5, wherein the first process to forecast the need to addprocessing capacity includes performing a cost-efficiency analysis todetermine a financial benefit associated with adding processingcapacity.
 7. The media of claim 6, wherein the cost-efficiency analysisincludes weighing a first cost associated with augmenting the secondcommunications trunk against a second cost associated with notaugmenting the second communications trunk.
 8. Computer-storage mediahaving computer-useable instructions embodied thereon for facilitating amethod of ensuring adequate data-processing capacity in a communicationsnetwork, the method comprising: identifying a plurality ofcommunications trunks to evaluate; determining whether a firstcommunications trunk has roll-over capacity; and when the firstcommunications trunk has roll-over capacity, then carrying out a firstprocess for the first communications trunk having roll-over capacity toforecast a need to add processing capacity; carrying out a secondprocess for a second communications trunk which does not have roll-overcapacity to forecast a need to add processing capacity, wherein thesecond process includes: (1) determining a maximum capacity that thesecond communications trunk is capable of handling, wherein determiningthe maximum capacity includes determining a percentage of an actualmaximum capacity, and (2) projecting a capacity-exhaustion timelinebased on the determined maximum capacity; projecting a date for each ofthe first communications trunk and the second communications trunk whencurrent processing capacity will be exhausted based on an accuracy ofprior projections; and providing a notification to an appropriate entityof the projected first process date and the projected second processdate.
 9. The media of claim 8, wherein the percentage of an actualmaximum capacity is about 80% of the maximum capacity that the secondcommunications trunk is capable of handling.
 10. The media of claim 8,wherein projecting the capacity-exhaustion timeline includes determininga peak offered-traffic amount for a given time interval.
 11. The mediaof claim 10, wherein projecting the capacity-exhaustion timeline furthercomprises repeating the step of determining a peak offered-trafficamount for a set of subsequent time intervals to accumulate a set ofdata points that indicate capacity over time.
 12. The media of claim 11,wherein projecting the capacity-exhaustion timeline further comprisesutilizing the set of data points to determine an initial timeline thatindicates when the second communications trunk will reach a certaincapacity.
 13. The media of claim 12, wherein projecting thecapacity-exhaustion timeline further comprises modifying the initialtimeline by a factor that is based on an accuracy of prior capacityprojections.
 14. The media of claim 1, wherein at least said secondprocess includes projecting a date when said current processing capacitywill be exhausted based on growth of traffic handled by the secondcommunications trunk and on said accuracy of prior projections. 15.Computer-storage media having computer-useable instructions embodiedthereon for facilitating a method of ensuring adequate data-processingcapacity in a communications network, the method comprising: identifyinga communications resource (resource) to evaluate; determining a maximumcapacity that the resource is capable of handling; comparing priorcapacity projections with historical performance data; based on a resultof the comparing, determining an adjustment factor that reflects aninaccuracy of the prior capacity projections; projecting acapacity-exhaustion timeline, wherein the timeline includes timeestimations that are adjusted by the adjustment factor; and providing anotification to an appropriate entity of the projectedcapacity-exhaustion timeline.
 16. The media of claim 15, wherein theadjustment factor is an average of the variances of prior projectionsfrom the actual values.